Identification of di-iso-nonyl phthalate metabolites for exposure marker discovery using signal mining strategy with high-resolution mass spectral data and its applications

Abstract

Di-iso-nonyl phthalate (DINP) is a widely used industrial plasticizer People come into contact with this chemical by using plastic products made with it Human health can be adversely affected by DINP exposure Exposure marker discovery of DINPs is crucial because of their high potential for human exposure and toxicity SMAIT(Signal Mining Algorithm with Isotope Tracing) mass defect filtering (MDF) and XCMS Online software were used for DINP exposure marker discovery in urine with an LTQ-Orbitrap high-resolution mass spectrometry (HRMS) dataset Sixteen 83 and 139 suspected DINP metabolite signals were obtained using the SMAIT MDF and XCMS procedures respectively Fourteen suspected metabolite signals mined simultaneously by the three metabolomics approaches were confirmed as DINP metabolites by structural information provided by LC-MS/MS Among them 13 suspected metabolite signals were validated as exposure-related markers in a rat model Because the body rapidly excretes DINP metabolites the use of spot urine analysis to assess long-term exposure may produce inconsistent results in epidemiologic studies Hair analysis has a useful place in biomonitoring particularly in estimating long-term or historical exposure for some chemicals DINP metabolites were measured in rat hair in animal experiments to evaluated long-term exposure to DINP The levels of DINP metabolites in rat hair had a positive correlation with increasing administered dose Significant positive correlations for MINP MOINP and MHINP were found between hair and urine (r = 0 86 r = 0 79 and r = 0 74 respectively p

Date of Award	2020
Original language	English
Supervisor	Pao-Chi Liao (Supervisor)